In medical practice, visual enhancement of color changes appearing on skin tissue can greatly aid medical practitioners in their diagnosis of underlying medical conditions such as peripheral neuropathy, for example. Methods for enhancing various aspects of images in this regard are highly desirable by diagnosticians.
Previous methods, such as that which is disclosed in “Filtering Source Video Data Via Independent Component Selection”, U.S. patent application Ser. No. 13/281,975, by Mestha et al., projected the source video onto a lower dimensional subspace and performed independent component analysis on the projected data to identify signal components of interest which were then used to reconstruct the video. However, such approaches do not work on a real-time basis since the algorithms performing Independent Component Analysis (ICA) are statistically-based methods which require large amounts of data and are computationally intensive. Adding to the problem is that signals of interest are often quite weak. For example, the amplitude of a cardiac pulse signal appearing on a facial region in the RGB channels is typically less than a single unit of a 0-255 color scale. Due to such a small signal-to-noise ratio (SNR), it can be difficult to detect a desired signal of interest appearing in local pixels directly from local signals. On the other hand, SNR can be improved by applying spatial filters, such as mean filters over large areas, but it still can be difficult to identify the source of a signal in low resolution images obtained by simply averaging, as in the ICA approach. What is desirable is to, not only enhancement of a signal of interest in video data, but also to ameliorate the SNR deficiency.
Accordingly, what is needed in this art is a system and method for processing source video to identify a time-series signal contained within that source video data and modify the intensity value of pixels in the image frames of that video which are associated with that signal such that, upon playback, the signal is visually enhanced.